جست‌وجوی استاد و پژوهشگر

Apurva Ratan Murtyمشاهده پروفایل

استادیار

• Visual Intelligence
• Neurodevelopmental Disorders
• fMRI
• +۴ مورد دیگر

Apurva Ratan Murty is an Assistant Professor at the School of Psychology, Georgia Institute of Technology. His research focuses on uncovering neural codes and algorithms underlying human vision by integrating biological studies with artificial intelligence models. The Murty Lab employs advanced neuroimaging techniques (fMRI, MEG, EEG) and computational methods (deep learning, generative AI) to explore perceptual development, neurodevelopmental disorders, and invariant object representations. He holds a PhD from the Indian Institute of Science (IISc) and completed postdoctoral research at MIT's McGovern Institute. Education: PhD in Neuroscience, Indian Institute of Science (IISc), Bangalore, India (Adviser: S.P. Arun) Postdoctoral Research: MIT's Center for Brains, Minds, and Machines (Advisers: Nancy Kanwisher, James DiCarlo) Research Interests: The lab investigates how visual information transforms into perception through neural circuits, emphasizing cross-disciplinary approaches to bridge biological and artificial vision systems. Key themes include modeling neurodevelopmental disorders, analyzing fMRI data with AI, and developing biologically constrained computational frameworks. Awards: NIH K99/R00 Pathway to Independence Award Young Systems Scientist Award, National Systems Society of India Institute Medal, Indian Institute of Science Labs & Teams: The Murty Lab actively recruits PhD students, postdocs, and undergraduates. The lab's work is showcased at murtylab.com , emphasizing translational research in vision science and neurotechnology.

Woon Ju Parkمشاهده پروفایل

استادیار

• plasticity
• typical and atypical development
• special populations
• +۳ مورد دیگر

Woon Ju Park is an Assistant Professor in the School of Psychology at the Georgia Institute of Technology, within the College of Sciences. His research focuses on understanding neural plasticity, sensory processing, and atypical development in special populations such as early blind individuals and those with autism spectrum disorder (ASD). He holds a Ph.D. in Brain and Cognitive Sciences from the University of Rochester, an M.S. in Cognitive Science from Yonsei University, and a B.A. in Psychology from Yonsei University. His research interests include studying how the brain adapts to sensory deprivation (e.g., blindness), the mechanisms of cross-modal plasticity, and perceptual deficits in developmental disorders. He employs multimodal neuroimaging techniques and psychophysical experiments to explore these topics. Recent work highlights enhanced auditory motion perception in early blind individuals and atypical visual processing in ASD. Dr. Park’s publications span 2011 to 2024, with a focus on sensory adaptation, neural reorganization, and translational neuroscience. His work bridges basic science and clinical applications, such as investigating transcranial stimulation to improve sensory perception. He leads the CORE Lab , which explores cognitive and perceptual mechanisms in health and disease. No scientific awards or grants are explicitly listed in the provided materials.

• Machine Learning
• Computer Vision
• Multimodal GenAI
• +۳ مورد دیگر

Xiatian Zhu is a Senior Lecturer at the University of Surrey's Faculty of Engineering and Physical Sciences, affiliated with the Surrey Institute for People-Centred AI and the Centre for Vision, Speech and Signal Processing (CVSSP). He holds a PhD from London and is a Fellow of the Higher Education Academy (FHEA). His research focuses on scalable machine learning, including self-supervised learning, multimodal GenAI, and computer vision applications for autonomous systems. Dr. Zhu's research interests span machine learning, computer vision, and multimodal generative AI, with emphasis on autonomous driving, 3D object detection, and domain adaptation. His work integrates vision-language models for open-set recognition and leverages novel neural architectures for real-time applications. His publications demonstrate a strong focus on autonomous driving perception, 3D generative modeling, and domain-agnostic AI. Key trends include multi-sensor fusion, self-supervised representation learning, and computationally efficient transformers. Recent works also explore audio-visual synthesis and adversarial robustness. Awards: 2016 Sullivan Doctoral Thesis Prize (Best UK Computer Vision Doctoral Thesis) He leads the X-Up Lab and collaborates extensively on grants related to AI safety, efficient 3D generation, and multimodal systems. His team focuses on real-world applications in intelligent transportation and human-centred AI.

Andrew Gilbertمشاهده پروفایل

دانشیار

• Computer Vision
• Machine Learning
• Generative Modelling
• +۶ مورد دیگر

Dr. Andrew Gilbert is an Associate Professor in Machine Learning at the University of Surrey, co-leading the Centre for Creative Arts and Technologies (C-CATS). His work bridges computer vision, generative AI, and creative industries, focusing on interpretable and human-centered systems. He leads a multidisciplinary team of 17 PhD students and has published over 100 papers in top venues like CVPR, ECCV, and BMVC. Education & Research: Gilbert earned his PhD in Computer Vision from the University of Surrey (2008) and has pioneered advancements in video understanding, style modeling, and AI-driven media production. His research integrates technical innovation with creative applications, such as AI-generated narratives and blockchain-based content rights. Research Trends: Recent work emphasizes long-term video analysis (DANTE-AD), motion-focused self-supervision (MOFO), and human-AI benchmarking (Human vs. Machine Minds). His projects often involve industry partnerships to address real-world challenges in media production and digital arts. Achievements: Best Paper at CVMP 2023, active BMVA committee member, and contributor to standards in explainable AI. His lab’s annual Gilbertine Weekend Away fosters collaborative research culture. Advising: Supervised 14 PhD completions, with ongoing projects in video diffusion models, decentralized content rights, and multimodal scene understanding. Seeks exceptional candidates for funded UK-based PhD opportunities. Key Labs: C-CATS lab explores AI’s creative applications, while his group develops tools for style transfer (NeAT), action recognition, and 3D performance capture.

Jean-Yves Guillemautمشاهده پروفایل

مدرس ارشد

• 3D Computer Vision
• 3D Reconstruction
• Computational Photography
• +۴ مورد دیگر

Dr. Jean-Yves Guillemaut is a Senior Lecturer in 3D Computer Vision and Postgraduate Research Director at the Centre for Vision, Speech and Signal Processing (CVSSP) within the School of Computer Science and Electronic Engineering at the University of Surrey. His research focuses on 3D reconstruction of complex dynamic scenes, particularly addressing challenges with glossy materials and uncontrolled outdoor environments. He collaborates with industry to apply his work in AR/VR, immersive content production, and free-viewpoint video. Education: He holds an MEng (hons), PhD, and is a member of professional bodies including MIEEE, MBMVA, and FHEA. His technical expertise spans computational photography, lightfield imaging, and artificial intelligence. Current PhD students: Matthew Bailey, Yue Zhang, Gianmarco Addari Laboratory affiliations: CVSSP Research interests include scene modeling from multi-view video, dynamic scene reconstruction, and applications in creative industries. Recent projects involve developing algorithms for illumination estimation, pedestrian tracking, and neural rendering. Grants and collaborations are centered on industry partnerships enhancing creative technology sectors. Future work aims to expand outdoor scene modeling and AI-driven medical imaging applications.

Marin Kobilarovمشاهده پروفایل

دانشیار

• Intelligent Robotics
• Autonomous Systems
• Mechanical Control Systems
• +۳ مورد دیگر

Marin Kobilarov is an Associate Professor in the Department of Mechanical Engineering at Johns Hopkins University’s Whiting School of Engineering. He leads the Laboratory for Autonomous Systems, Control, and Optimization, a subset of the Laboratory for Computational Sensing and Robotics. His research focuses on intelligent robotic vehicles capable of autonomous navigation and task execution in complex environments, with applications in aerial vehicles, medical robotics, and space systems. Dr. Kobilarov holds a BS in Computer Science and Applied Mathematics from Trinity College (2003) and a PhD in Computer Science from the University of Southern California (2008). Before joining Johns Hopkins in 2012, he completed postdoctoral fellowships at the California Institute of Technology and the United Technologies Research Center, specializing in control systems and dynamical systems. His research interests span robotics, autonomous systems, and medical applications, including surgical robotics for ophthalmology and microsurgery. Notable projects include developing perception-aware navigation frameworks, UAV pick-and-place systems, and autonomous retinal surgery tools using imitation learning and real-time imaging. His publications emphasize trajectory optimization, multi-agent coordination, and safety-critical control, with recent work addressing challenges in occluded environment navigation and uncertainty-aware planning for heterogeneous robot teams. Awards include prestigious postdoctoral fellowships from the Keck Institute for Space Studies and United Technologies Research Center. Dr. Kobilarov’s lab collaborates across disciplines to advance robotics in dynamic environments, with ongoing work on surgical automation, autonomous aerial systems, and robust control algorithms. His research bridges theoretical control methods with practical engineering solutions for real-world robotic applications.

Miles Hansardمشاهده پروفایل

مدرس ارشد

• Computer Vision
• Robotics
• 3D Displays
• +۷ مورد دیگر

Miles Hansard is a Senior Lecturer in the School of Electronic Engineering & Computer Science at Queen Mary, University of London. He is affiliated with the Vision Group and the ARQ Centre for Advanced Robotics. His research focuses on computer vision, robotics, and 3D imaging technologies, including depth cameras, binocular vision, and scene reconstruction. He has contributed to projects like the GRIP Software Framework for robotics integration and Gaussian Process models for UAV localization. His work spans medical imaging applications, such as analyzing eye movements after vision loss, and explores discomfort factors in stereoscopic displays. Hansard has published extensively in journals like IEEE Transactions and the Royal Society Proceedings, and his research often addresses sensor fusion, calibration, and computational photography. He holds a primary email address at miles.hansard@qmul.ac.uk.

Josephine Sullivanمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۴ مورد دیگر

Josephine Sullivan is an Associate Professor at KTH Royal Institute of Technology, affiliated with the Division of Robotics, Perception and Learning. She is a core faculty member of Digital Futures, a cross-disciplinary research center addressing global challenges through digital technologies. Her research focuses on AI-driven environmental monitoring, disaster management, and deep learning applications in computer vision and remote sensing. She has supervised projects such as the fusion of radar and optical remote sensing for wildfire monitoring and contributes to initiatives like EO-AI4GlobalChange. Sullivan teaches advanced courses in deep learning, generative models, and machine learning at KTH, emphasizing hands-on training and research integration. Her work bridges theoretical advancements in neural networks with practical solutions for climate-related disasters, leveraging satellite imagery and time-series data. Recent projects highlight innovations in wildfire prediction datasets (Wildfirespreadts) and robust networks for disaster damage assessment (DisasterAdaptiveNet). She collaborates across disciplines at Digital Futures, contributing to labs and teams focused on AI ethics, sustainable technologies, and global change mitigation. Sullivan’s academic leadership includes roles as course examiner and responsible for multiple graduate programs in computer science and engineering.

Wenwu Wangمشاهده پروفایل

استاد

• Signal Processing
• Machine Learning
• Artificial Intelligence
• +۴ مورد دیگر

Wenwu Wang is a Professor of Signal Processing and Machine Learning at the University of Surrey's School of Computer Science and Electronic Engineering, and serves as Associate Head (External Engagement). He is Co-Director of the Machine Audition Lab within CVSSP and an AI Fellow at Surrey's Institute for People Centred Artificial Intelligence. Education: Holds a BSc, MSc, and PhD (specific disciplines not specified). Research focuses on unsupervised learning techniques , computational auditory scene analysis , and multimodal machine learning . Notable projects include audio captioning systems, text-to-audio generation (e.g., AudioLDM), and anomalous sound detection frameworks. Grants: £30M+ total portfolio (as PI: £2.2M; as CI: £14M+). Funders include EPSRC, MoD, EU, and industry partners like BBC/Huawei. Key Projects: AudioTurbo (fast text-to-audio), SemantiCodec (ultra-low bitrate audio), DCASE Challenge contributions. Lab Leadership: Machine Audition Lab explores intersections of audio perception and AI. Recent work emphasizes large audio models , cross-modal generation, and real-world applications in aquaculture monitoring and urban soundscapes.

Daniel Patelمشاهده پروفایل

پژوهشگر

• Geological Visualization
• Computer Graphics
• Scientific Visualization
• +۳ مورد دیگر

Daniel Patel is an Adjunct Researcher at the University of Bergen's Department of Informatics. His work focuses on advancing visualization techniques for geological and geophysical data, with a particular emphasis on interactive modeling, real-time rendering, and domain-specific tools for scientific communication. He has contributed to projects such as 'Geological Storytelling' and 'Rapid Sketch-Based Modeling,' which aim to bridge the gap between geologists' conceptual ideas and computational modeling. Patel’s research integrates computer graphics innovations with domain-specific challenges in geology and marine science. His research interests include volume rendering, sketch-based modeling, seismic data interpretation, and educational approaches to PhD training. He collaborates with interdisciplinary teams, including the VisGroup at UiB, to develop tools for visualizing complex geological structures and sonar data. Patel holds a PhD in expressive visualization from TU Wien (2009) and has published extensively on topics ranging from chromatic shadows in medical visualization to statistical transfer functions for noisy data. Key contributions include the development of occlusion shading models, instant convolution shadows, and knowledge-assisted visualization frameworks for seismic exploration. His work emphasizes rapid prototyping and user-centric design, enabling domain experts to rapidly communicate and explore geological hypotheses.

Richard Aslinمشاهده پروفایل

استاد پژوهشی

• Statistical Learning
• Developmental Psychology
• Cognitive Neuroscience
• +۳ مورد دیگر

Richard Aslin, Ph.D., is a Senior Scientist at Haskins Laboratories, an independent research institution affiliated with Yale University. He joined Haskins in 2017 to re-establish the BabyLab, focusing on developmental research in infant cognition and statistical learning mechanisms. His work explores how infants implicitly learn from environmental stimuli without prior knowledge, emphasizing statistical learning processes across domains like speech, vision, and motor skills. His research employs advanced neuroimaging techniques such as fMRI, EEG, and fNIRS to study brain function in infants and adults. Notable projects include investigating spoken word recognition via eye-tracking and neural decoding, as well as studying functional connectivity changes during learning. Aslin leads grants funded by NIH and the Gates Foundation, focusing on statistical learning, word recognition, and neurodevelopmental outcomes. Key collaborators include David Lewkowicz (BabyLab co-director) and Elizabeth Newport. He mentors a team of researchers, including postdoctoral fellows and Yale graduate students. His findings bridge cognitive and developmental neuroscience, highlighting the neural underpinnings of early learning and its lifelong implications.

Katie Grayمشاهده پروفایل

دانشیار

• Social Perception
• Cognitive Neuroscience
• Face Recognition
• +۳ مورد دیگر

Katie Gray is an Associate Professor at the University of Reading’s School of Psychology and Clinical Language Sciences. Her research focuses on social perception, particularly exploring how visual cues influence processing of social information using EEG. She investigates topics such as face recognition, autism spectrum disorder, and the perception of interpersonal interactions. Her work integrates cognitive neuroscience methods with behavioral studies to understand social cognition mechanisms. Recent studies include investigations into masked face identification, emotional expression recognition in autism, and the role of dyadic arrangements in visual perception. Dr. Gray collaborates on projects within the CINN research group, advancing understanding of neural and behavioral bases of social interaction processing. Key research areas include social attention, facial perception, and developmental differences in social cognition. Her contributions bridge clinical and basic research, addressing practical applications in forensic science and mental health diagnostics. Despite no listed awards or grants in the provided text, her extensive publication record reflects sustained academic engagement in these domains.

Adam Snyderمشاهده پروفایل

استادیار

• Visual Attention Mechanisms
• Neural Dynamics
• Cognitive Neuroscience
• +۳ مورد دیگر

Adam Snyder is an Assistant Professor in the Department of Brain and Cognitive Sciences at the University of Rochester, School of Arts & Sciences. His research focuses on understanding the computational mechanisms that allow the brain to process goal-relevant visual information while filtering distractions, examining both small neuronal populations and large-scale cortical coordination. His work spans micro- and macro-scale mechanisms of visual attention, aiming to develop interventions for visual navigation challenges. Research Interests include: Neural dynamics underlying visual attention Cortical coordination and population activity False alarm mechanisms in visual processing Generative models of visual scenes EEG and neural oscillations in attention His publications highlight advancements in understanding attentional coding in prefrontal and visual cortices, dynamic neuronal signaling in V4, and global arousal signatures via EEG. While no awards or grants are explicitly listed, his work contributes significantly to computational and experimental neuroscience. He can be contacted at adam.snyder@rochester.edu or through his office at 318 Meliora Hall.

Miao Wangمشاهده پروفایل

دانشیار

• Computer Graphics
• Virtual Reality
• Augmented Reality
• +۱۲ مورد دیگر

Miao Wang is an Associate Professor at the State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beihang University. He earned his PhD in Computer Science and Technology from Tsinghua University in 2016 under the supervision of Prof. Shi-Min Hu, following a bachelor's degree from Xidian University in 2011. He conducted postdoctoral research at Tsinghua University from 2016 to 2018 before joining Beihang University in 2018. PhD in Computer Science and Technology, Tsinghua University (2011–2016) Bachelor of Computer Science and Technology, Xidian University (2007–2011) His primary research interests include computer graphics, virtual and augmented reality, visual media computing, and computer vision. He focuses on 3D AIGC for extended reality (XR), digital avatars, redirected locomotion, and immersive collaborative interaction in VR/AR environments. His work bridges algorithmic innovation with practical immersive applications. The recent publications highlight a strong trend in neural rendering, avatar synthesis, and intelligent interaction in virtual environments. Key themes include 3D Gaussian representations, neural fields for real-time avatar generation, semantic-aware locomotion adaptation, and redirected walking techniques. These works are published in top venues such as CVPR, IEEE TVCG, ACM TOG, and IEEE VR, reflecting cutting-edge contributions in graphics and immersive systems. IEEE VR 2025 Best Paper Award CAD/Graphics 2023 Best Paper Award CVMJ 2019 Honorable Mention Award National Science and Technology Progress Award (2nd Prize, rank 9) Higher Education Teaching Achievement Incentive Program (Special Class, rank 2) Xiaomi Young Scholar Beihang University Young Talent Miao Wang actively supervises PhD and master’s students, hosts undergraduate interns, and leads a vibrant research team. He has secured funding from the National Natural Science Foundation of China and participates in key national and regional projects. His team maintains strong international collaborations with researchers from Tsinghua University, Cardiff University, Universität Hamburg, and others. He also contributes to the academic community through service on program committees of IEEE VR, ISMAR, Pacific Graphics, and CVM, and holds leadership roles in the China Computer Society and China Graphics Society. He leads research in the State Key Laboratory of Virtual Reality Technology and Systems at Beihang University, where his team develops tools and frameworks for immersive media, including OpenRDW, a library for redirected walking. The group fosters innovation through interdisciplinary collaboration and international engagement.

• Image Processing
• Machine Learning
• Computer Vision
• +۴ مورد دیگر

Saturnino Maldonado Bascón is a full Professor at the Universidad de Alcalá, affiliated with the Signal Theory and Communications Department. His research focuses on advanced signal processing techniques, machine learning applications, and computer vision systems. He earned his doctorate from Universidad de Alcalá in 1999 with a thesis on multiresolution analysis for image compression. Key research interests include traffic sign recognition, video surveillance algorithms, 3D object recognition, and noise reduction in digital images. His work frequently employs support vector machines (SVM), spatial pyramids, and clustering methods to address challenges in robotics, autonomous systems, and sensor data analysis. Publications highlight contributions to vehicle tracking, odometry correction in differential robots, and efficient video annotation techniques. His research group (GRAM) develops multisensorial analysis solutions for intelligent infrastructure systems like the WHISNU platform. Maldonado has explored diverse applications from biomedical signal processing to traffic management systems through grants and collaborative projects. No scientific awards are explicitly mentioned, but his extensive publication record reflects sustained academic impact. He leads a research team focused on real-time systems, sensor fusion, and computer vision applications in both academic and industrial contexts.